class_docstr: A regularizer that applies a L1 regularization penalty.
  
  The L1 regularization penalty is computed as:
  `loss = l1 * reduce_sum(abs(x))`
  
  L1 may be passed to a layer as a string identifier:
  
  >>> dense = Dense(3, kernel_regularizer='l1')
  
  In this case, the default value used is `l1=0.01`.
  
  Arguments:
      l1: float, L1 regularization factor.
code: def __init__(self, l1=0.01):
      l1 = 0.01 if l1 is None else l1
      validate_float_arg(l1, name="l1")
      self.l1 = ops.convert_to_tensor(l1)
  
imports: import math
  from keras import ops
  from keras.api_export import keras_export
  from keras.utils.numerical_utils import normalize
label_desc: Use image node_id 1 to create a new L1 object from inherited base classes: Regularizer with example: obj = L1(l1)